Abstract
The quality and success of an organization are inherently linked to employee performance, making periodic performance evaluation essential. This article analyzes the most commonly used existing models of performance evaluation at the organizational level. Traditional performance evaluation models, such as competency assessment, 360-degree feedback, and critical incident evaluation, have advantages and disadvantages. For example, competency assessment allows measuring skills and behaviors, while 360-degree feedback offers a comprehensive view by considering multiple perspectives. However, many performance evaluation models are affected by biases and subjectivity in results, requiring a significant investment of time and resources. On the other hand, artificial intelligence is transforming the landscape of performance evaluation. Technological advances enable the development of more objective and efficient systems for assessing employee performance. Additionally, artificial intelligence can analyze large volumes of data to identify trends and patterns in performance.
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